• Journal of Distribution Research
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• v.10 no.3
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• pp.1-14
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• 2005

It is important to control performance and a Sustainable Collaboration (SC) for the successful Supply Chain Management (SCM). This research developed a control model which analyzed SCM performances based on a Balanced Scorecard (ESC) and an SC using Support Vector Machine (SVM). 108 specialists of an SCM completed the questionnaires. We analyzed experimental data set using SVM. This research compared the forecasting accuracy of an SCMSC through four types of SVM kernels: (1) linear, (2) polynomial (3) Radial Basis Function (REF), and (4) sigmoid kernel (linear > RBF > Sigmoid > Polynomial). Then, this study compares the prediction performance of SVM linear kernel with Artificial Neural Network. (ANN). The research findings show that using SVM linear kernel to forecast an SCMSC is the most outstanding. Thus SVM linear kernel provides a promising alternative to an SC control level. A company which pursues an SCM can use the information of an SC in the SVM model.

• Journal of Korea Technology Innovation Society
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• v.19 no.4
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• pp.687-710
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• 2016

This study aims to empirically estimate distributed lag effects of expenditure by type of R&D on scientific publication in the national R&D program. To analyze the lag structure between them, we used a dataset comprised of panel data from 104 technologies categorized by 6T (IT, BT, NT, ST, ET, CT) from 2007 to 2014, and employed multiple regression analysis based on the polynomial distributed lag model. This is because it is highly likely to emerge multicollinearity, if a distributed lag model without special restrictions is applied to multiple regression analysis. The main results are as follows. In the case of basic research, its lag effects are relatively evenly distributed during four years. On the other hand, the applied research and experimental development have distributed lag effects for three years and two years respectively. Therefore, when it comes to analyzing performance of scientific publication, it is necessary to be performed with characteristics of the time lag by type of R&D.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.11
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• pp.1217-1224
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• 2009

This paper newly proposes a methodology towards computing antenna element weights which are satisfying asymmetric sidelobe levels(SLLs) specified arbitrarily on both sides of the main beam pattern, in the linear phased array antenna pattern synthesis problem. Opposite to the conventional methods in which the element weights are directly optimized from the array factor, this method is based on the optimum perturbations of complex roots inherent to the Schelkunoff's polynomial form which is described for the array factor. From the proposed methodology, the capability of nulling the directions of multiple jammers is also possible by independently perturbing only the complex roots corresponding to each jamming direction, hence allowing an enhancement of the simplicity of the numerical procedure by means of a proper reduction of the dimension of the solution space. The complex weights over the array are then easily computed by substituting the optimally perturbed complex roots to the Schelkunoff's polynomial. Some examples are examined and numerically verified by substituting the extracted weights into the array factor equation.

• Journal of Korea Water Resources Association
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• v.40 no.7
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• pp.511-521
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• 2007

The objective of this study is to develop a method of rainfall adjustment on duration and topographic elevation for rainfall data in Jejudo. The method of rainfall adjustment is based on the polynomial regression analysis for the hourly rainfall data and the distribution of observatories of korea meteorological administration. As the results of modeling have shown, duration and rainfall are more correlated than topographic elevation and rainfall, and the model which considers only an elevation exaggerates the amount of rainfall adjustment. Hence the model of duration-elevation-rainfall is more competitive to the natural rainfall event than the model of topographic elevation-rainfall. However this model require to supplement a small number of rainfall observatories and short observed period.

• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.18-27
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• 2023

In this paper, the structural-thermal-optical performance analysis of the mirror was performed by setting the laser heat source as the boundary condition of the thermal analysis. For the laser heat source model, the Beer-Lambert model considering semi-transparent optical material based on Gaussian beam was selected as the boundary condition, and the mechanical part was not considered, to analyze the performance of only the mirror. As a result of the thermal analysis, thermal stress and thermal deformation data due to temperature change on the surface of the mirror were obtained. The displacement data of the surface due to thermal deformation was fitted to a Zernike polynomial to calculate the optical performance, through which the performance of the mirror when a high-energy laser was incident on the mirror could be predicted.

• Structural Engineering and Mechanics
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• v.84 no.2
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• Journal of Biosystems Engineering
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• v.40 no.1
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• pp.89-93
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• 2015

Purpose: Machine vision-based image processing methods can be useful for estimating the fresh weight of plants. This study analyzes the ability of two different image processing methods, i.e., morphological and pixel-value analysis methods, to measure the fresh weight of lettuce grown in a closed hydroponic system. Methods: Polynomial calibration models are developed to relate the number of pixels in images of leaf areas determined by the image processing methods to actual fresh weights of lettuce measured with a digital scale. The study analyzes the ability of the machine vision- based calibration models to predict the fresh weights of lettuce. Results: The coefficients of determination (> 0.93) and standard error of prediction (SEP) values (< 5 g) generated by the two developed models imply that the image processing methods could accurately estimate the fresh weight of each lettuce plant during its growing stage. Conclusions: The results demonstrate that the growing status of a lettuce plant can be estimated using leaf images and regression equations. This shows that a machine vision system installed on a plant growing bed can potentially be used to determine optimal harvest timings for efficient plant growth management.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• Structural Engineering and Mechanics
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• v.61 no.1
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• pp.161-169
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• 2017

This study aimed to develop a simple and effective method to facilitate the experimental research on mechanical properties of masonry under biaxial compressive stress. A series of tests on full-scale brick masonry panels under biaxial compression have been performed in limited principal stress ratios oriented at various angles to the bed joints. Failure modes of tested panels were observed and failure features were analyzed to reveal the mechanical behavior of masonry under biaxial compression. Based on the experimental data, the failure curve in terms of two orthotropic principal stresses has been presented and the failure criterion of brick masonry in the form of the tensor polynomial has been established, which indicate that the anisotropy for masonry is closely related to the difference of applied stress as well as the orientation of bed joints. Further, compared with previous failure curves and criteria for masonry, it can be found that the relative strength of mortar and block has a considerable effect on the degree of anisotropy for masonry. The test results demonstrate the validity of the proposed experimental method for the approximation of masonry failure under biaxial compressive stress and provide valuable information used to establish experimentally based methodologies for the improvement of masonry failure criteria.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.217-223
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• 2024

The current study aimed to evaluate the effectiveness of using Support Vector Machine (SVM) for political affiliation classification. The system was designed to analyze the political tweets collected from Twitter and classify them as positive, negative, and neutral. The performance analysis of the SVM classifier was based on the calculation of metrics such as accuracy, precision, recall, and f1-score. The results showed that the classifier had high accuracy and f1-score, indicating its effectiveness in classifying the political tweets. The implementation of SVM in this study is based on the principle of Structural Risk Minimization (SRM), which endeavors to identify the maximum margin hyperplane between two classes of data. The results indicate that SVM can be a reliable classification approach for the analysis of political affiliations, possessing the capability to accurately categorize both linear and non-linear information using linear, polynomial or radial basis kernels. This paper provides a comprehensive overview of using SVM for political affiliation analysis and highlights the importance of using accurate classification methods in the field of political analysis.